The present section is intended to introduce the reader to various aspects of art, which may be related to various aspects of the present principles that are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present principles. Accordingly, it should be understood that these statements are to be read in this light, and not as admissions of prior art.
In recent decades, there is an important demand for three-dimensional (“3D”) content for computer graphics and virtual reality applications. Image-based 3D reconstruction is a technique which makes it possible to obtain a 3D representation of a real object or a real scene (i.e. a 3D model) from a set of images acquired from different points of view of the real object or the real scene. More generally, the aim is to determine, from the set of images, the two-dimensional (“2D”) coordinates of visible points on these images in a 3D real space referential.
Because a conventional image capture device acquires images which are projections from 3D scene onto a 2D plane (image sensor), depth information is lost during the acquisition process. A 3D point corresponding to a given image point is constrained to be on the line of sight. From a single image, it is therefore impossible to determine which point on this line corresponds to the image point. If two images acquired from different points of view are available, then the position of a 3D point can be found as the intersection of the two projection rays. Therefore, to obtain a 3D representation, a stereoscopic imaging equipment is usually used, such as a pair of digital cameras for example.
The creation of 3D representation of a scene from multiple images (“backward projection process”) is the reverse process of obtaining 2D images from real scene (“forward projection process”). There is therefore a need to understand how the 3D scene is mapped into 2D image for each of the cameras in order to infer 3D representation of the scene from the 2D images acquired by these cameras. This is why a precise description of image formation process for each camera is needed. This is referred to as camera calibration.
Camera calibration consists of determining the relation between the 3D coordinates of 3D points of the scene and the 2D coordinates of their projection in the image (referred to as images points). This is the starting point of the image-based 3D reconstruction process. Camera calibration is an important step to obtain, from acquired images, precise metric information, especially for dimensional measurement applications. Indeed, the optical system of the camera (also commonly referred to as “main lens”) is a complex multi-lens system based on a particular arrangement of optical lenses, and it is necessary to take into account defaults (e.g. geometrical distortions) induced by this optical system.
In order to model the process of formation of images of a camera, different known calibration models have been proposed. Among these models, there are the pinhole model (also called central projection model), thin lens model and thick lens model.
Perform a calibration using pinhole model consists in estimating the transformation for making the transition from 3D coordinates of point P(x3d, y3d, z3d) of the scene in the object referential to 2D coordinates of the image I(x2d,y2d) associated with this point expressed in the image referential. This transformation can be expressed in the form of a multi-dimensional matrix, which comprises the parameters of the model.
The calibration model of the camera is defined by the characteristic intrinsic parameters of the camera. For dimensional measurement applications, it may be also necessary to know extrinsic parameters, i.e. the camera position and orientation in an object coordinate system.
However, pinhole model is based on a virtual location of image sensor plane. So, the backward projection process to remap an object point cloud from an acquired image requires acknowledgment of the distance between the object plane and the virtual image sensor plane. This model cannot respect the imaging system geometry (geometry of the sensor-lens-object triplets), making the backward projection process unreliable.
Another known calibration model using a set of seven intrinsic parameters has been proposed to improve the model accuracy. This known calibration model takes into account the location of entrance and exit pupils associated with the aperture iris diaphragm of the camera (also commonly referred to as “aperture stop”). In a general manner, the aperture iris diaphragm limits the size of the light bundle through the optical system. The pupil is the conjugate of the aperture iris diaphragm of the optical system. It corresponds to the surface, limited by the aperture iris diaphragm, by which a light bundle passes through the optical system. The pupil is said “entrance pupil” in object space and corresponds to the image of the aperture iris diaphragm through the upstream part of the optical system. The pupil is said “exit pupil” in image space and corresponds to the image of the aperture iris diaphragm through the downstream part of the optical system.
The set of seven parameters needed for that known model is: location of the primary and secondary principal planes of the optical system, location of the entrance and exit pupils, the size of the entrance and exit pupils, and the distance between the secondary principal plane and the image plane. This model requires determining manually a high number of parameters, some of which are difficult to assess precisely (like the location of primary and secondary principal planes). This kind of model is complex to implement in practice regardless of the optical formula of the optical system.
There is therefore a need for enhancing the modelling process of an imaging device.